Over 35 specialists and scholars will be invited by the joint NSF project team, consisting of PI Ming-Hsiang Tsou (Geography) and Co-PIs from both projects: Dipak Gupta (Political Science), Jean Mark Gawron (Linguistics), Brian Spitzberg (Communication), Li An (Geography), Heather Corliss (Public Health), Jay Lee (Geography, Kent State), Ruoming Jin (Computer Science, Kent State), Xinyue Ye (Geography, Kent State), and Xuan Shi (Geosciences, U of Arkansas). The meeting will include plenary presentations by invited experts, lightning talks, and focus group discussion of the issues. This workshop will generate a final report to be published on the IBSS project website. This year, our research theme will be “Representing Human Dynamics with Big Data, Social Media, and Social Networks in Hyperlocal Contexts”. The dynamic supply of big data from millions of social media messages, GPS tracks, medical records, wireless sensors, electronic health records, web pages, and cellular phones, becomes an important research domain today. Big data provide untapped potentials for discovering and analyzing dynamic human problems, including business analytics, disease outbreaks, traffic patterns, urban dynamics, bioinformatics, and environmental changes. Such data offer golden opportunities for scientists to develop new tools, new methods, and new theories. However, Big Data Science requires transdisciplinary collaboration and research methodologies to integrate multiple perspectives into collaborative research endeavors. This workshop will build a collaborative platform for scientists and researchers to work together. Specific research questions to be addressed in the workshop may include:

What are the unique characteristics of big data and social media in the context of hyperlocal human dynamics?

How can we analyze non-normal probability distributions and dynamic patterns of big data, social media, and social networks from a spatiotemporal perspective?

What type of innovative research frameworks can help us to collect, analyze, visualize and predict hyperlocal human dynamics using social media and big data?

What are the limitations of traditional GIS database and spatial statistics in analyzing big data and social media?

To what extent, and how, should theory and/or hypothesis-testing drive big data, or big data drive theory--what is the role of theory in relation to big data?

To what extent can social network analyses yield (dis)confirming results in regard to theory?

Given time-series and potential nonlinearity of spatial data (big data), what statistical methods, measures or statistics can be developed and/or used to "dig out" the space-time pattern(s) in human dynamics, and describe/characterize/explain/predict such patterns?

The workshop participants will be invited by the CDI and IBSS project PI and Co-PIs. The NSF projects will cover participants’ travel and lodging costs (but no stipends). Invitees are required to submit a two page resume (biosketch, NSF style) and a two page position papers for related topics.

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Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks is funded by National Science Foundation, Division of Computer and Network Systems, NSF IBSS Award Abstract #1416509. Opinions expressed are those of the authors and not necessarily those of the National Science Foundation. NSF Award Information